Lab11x
Andrea Dame
11/15/2020
###Ordination Plots #Loading packages and Preparing data
library("phyloseq"); packageVersion("phyloseq")## [1] '1.32.0'data(GlobalPatterns)
library("ggplot2"); packageVersion("ggplot2")## [1] '3.3.2'library("plyr"); packageVersion("plyr")## [1] '1.8.6'theme_set(theme_bw())#Removing OTUs that do not show appear more than 5 times in more than half the samples
GP = GlobalPatterns
wh0 = genefilter_sample(GP, filterfun_sample(function(x) x > 5), A=0.5*nsamples(GP))
GP1 = prune_taxa(wh0, GP)GP1 = transform_sample_counts(GP1, function(x) 1E6 * x/sum(x))phylum.sum = tapply(taxa_sums(GP1), tax_table(GP1)[, "Phylum"], sum, na.rm=TRUE)
top5phyla = names(sort(phylum.sum, TRUE))[1:5]
GP1 = prune_taxa((tax_table(GP1)[, "Phylum"] %in% top5phyla), GP1)human = get_variable(GP1, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")
sample_data(GP1)$human <- factor(human)Four Main Ordination Plots
#Just OTUs
GP.ord <- ordinate(GP1, "NMDS", "bray")## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1333468
## Run 1 stress 0.1465222
## Run 2 stress 0.1518736
## Run 3 stress 0.1614548
## Run 4 stress 0.1385323
## Run 5 stress 0.1662862
## Run 6 stress 0.1497848
## Run 7 stress 0.1797956
## Run 8 stress 0.15429
## Run 9 stress 0.1650392
## Run 10 stress 0.168475
## Run 11 stress 0.1385323
## Run 12 stress 0.1471619
## Run 13 stress 0.1333468
## ... New best solution
## ... Procrustes: rmse 4.238967e-06 max resid 1.256472e-05
## ... Similar to previous best
## Run 14 stress 0.1477687
## Run 15 stress 0.1576494
## Run 16 stress 0.163672
## Run 17 stress 0.1658226
## Run 18 stress 0.1882864
## Run 19 stress 0.180736
## Run 20 stress 0.1333468
## ... Procrustes: rmse 1.337141e-06 max resid 2.635438e-06
## ... Similar to previous best
## *** Solution reachedp1 = plot_ordination(GP1, GP.ord, type="taxa", color="Phylum", title="taxa")
print(p1)
p1 + facet_wrap(~Phylum, 3)
# Just Samples
p2 = plot_ordination(GP1, GP.ord, type="samples", color="SampleType", shape="human")
p2 + geom_polygon(aes(fill=SampleType)) + geom_point(size=5) + ggtitle("samples")
#Biplot graphic
p3 = plot_ordination(GP1, GP.ord, type="biplot", color="SampleType", shape="Phylum", title="biplot")
# Some stuff to modify the automatic shape scale
GP1.shape.names = get_taxa_unique(GP1, "Phylum")
GP1.shape <- 15:(15 + length(GP1.shape.names) - 1)
names(GP1.shape) <- GP1.shape.names
GP1.shape["samples"] <- 16
p3 + scale_shape_manual(values=GP1.shape)
#Split Graphic
p4 = plot_ordination(GP1, GP.ord, type="split", color="Phylum", shape="human", label="SampleType", title="split")
p4
gg_color_hue <- function(n){
hues = seq(15, 375, length=n+1)
hcl(h=hues, l=65, c=100)[1:n]
}
color.names <- levels(p4$data$Phylum)
p4cols <- gg_color_hue(length(color.names))
names(p4cols) <- color.names
p4cols["samples"] <- "black"
p4 + scale_color_manual(values=p4cols) #Supported Ordination Methods
dist = "bray"
ord_meths = c("DCA", "CCA", "RDA", "DPCoA", "NMDS", "MDS", "PCoA")
plist = llply(as.list(ord_meths), function(i, physeq, dist){
ordi = ordinate(physeq, method=i, distance=dist)
plot_ordination(physeq, ordi, "samples", color="SampleType")
}, GP1, dist)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1333468
## Run 1 stress 0.1492516
## Run 2 stress 0.1459312
## Run 3 stress 0.157644
## Run 4 stress 0.1385323
## Run 5 stress 0.3685524
## Run 6 stress 0.148898
## Run 7 stress 0.1333468
## ... New best solution
## ... Procrustes: rmse 1.327423e-06 max resid 3.540939e-06
## ... Similar to previous best
## Run 8 stress 0.1560954
## Run 9 stress 0.1510167
## Run 10 stress 0.1733957
## Run 11 stress 0.1570722
## Run 12 stress 0.1469145
## Run 13 stress 0.144908
## Run 14 stress 0.1465222
## Run 15 stress 0.2637735
## Run 16 stress 0.1529686
## Run 17 stress 0.1761351
## Run 18 stress 0.1758878
## Run 19 stress 0.1385324
## Run 20 stress 0.1612829
## *** Solution reachednames(plist) <- ord_methspdataframe = ldply(plist, function(x){
df = x$data[, 1:2]
colnames(df) = c("Axis_1", "Axis_2")
return(cbind(df, x$data))
})
names(pdataframe)[1] = "method"p = ggplot(pdataframe, aes(Axis_1, Axis_2, color=SampleType, shape=human, fill=SampleType))
p = p + geom_point(size=4) + geom_polygon()
p = p + facet_wrap(~method, scales="free")
p = p + scale_fill_brewer(type="qual", palette="Set1")
p = p + scale_colour_brewer(type="qual", palette="Set1")
p
plist[[2]]
p = plist[[2]] + scale_colour_brewer(type="qual", palette="Set1")
p = p + scale_fill_brewer(type="qual", palette="Set1")
p = p + geom_point(size=5) + geom_polygon(aes(fill=SampleType))
p
#MDS (“PCoA”) on Unifrac Distances
ordu = ordinate(GP1, "PCoA", "unifrac", weighted=TRUE)
plot_ordination(GP1, ordu, color="SampleType", shape="human")
p = plot_ordination(GP1, ordu, color="SampleType", shape="human")
p = p + geom_point(size=7, alpha=0.75)
p = p + scale_colour_brewer(type="qual", palette="Set1")
p + ggtitle("MDS/PCoA on weighted-UniFrac distance, GlobalPatterns")
###Alpha Diversity graphics #Load packages and set parameters
library("phyloseq"); packageVersion("phyloseq")## [1] '1.32.0'data("GlobalPatterns")library("ggplot2"); packageVersion("ggplot2")## [1] '3.3.2'theme_set(theme_bw())
pal = "Set1"
scale_colour_discrete <- function(palname=pal, ...){
scale_colour_brewer(palette=palname, ...)
}
scale_fill_discrete <- function(palname=pal, ...){
scale_fill_brewer(palette=palname, ...)
}#Prepare data
GP <- prune_species(speciesSums(GlobalPatterns) > 0, GlobalPatterns)## Warning: 'prune_species' is deprecated.
## Use 'prune_taxa' instead.
## See help("Deprecated") and help("phyloseq-deprecated").## Warning: 'speciesSums' is deprecated.
## Use 'taxa_sums' instead.
## See help("Deprecated") and help("phyloseq-deprecated").#Plot examples
plot_richness(GP)
plot_richness(GP, measures=c("Chao1", "Shannon"))
plot_richness(GP, x="SampleType", measures=c("Chao1", "Shannon"))
plot_richness(GP, x="SampleType", measures=c("Chao1", "Shannon"))
sampleData(GP)$human <- getVariable(GP, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")## Warning: 'getVariable' is deprecated.
## Use 'get_variable' instead.
## See help("Deprecated") and help("phyloseq-deprecated").## Warning: 'sampleData' is deprecated.
## Use 'sample_data' instead.
## See help("Deprecated") and help("phyloseq-deprecated").## Warning: 'sampleData<-' is deprecated.
## Use 'sample_data<-' instead.
## See help("Deprecated") and help("phyloseq-deprecated").plot_richness(GP, x="human", color="SampleType", measures=c("Chao1", "Shannon"))
GPst = merge_samples(GP, "SampleType")
# repair variables that were damaged during merge (coerced to numeric)
sample_data(GPst)$SampleType <- factor(sample_names(GPst))
sample_data(GPst)$human <- as.logical(sample_data(GPst)$human)p = plot_richness(GPst, x="human", color="SampleType", measures=c("Chao1", "Shannon"))
p + geom_point(size=5, alpha=0.7)
#More detail about ggplot2
p$layers## [[1]]
## geom_point: na.rm = TRUE
## stat_identity: na.rm = TRUE
## position_identity
##
## [[2]]
## mapping: ymax = ~value + se, ymin = ~value - se
## geom_errorbar: na.rm = FALSE, orientation = NA, width = 0.1, width = 0.1, flipped_aes = FALSE
## stat_identity: na.rm = FALSE
## position_identityp$layers <- p$layers[-1]
p + geom_point(size=5, alpha=0.7)
###Heatmap plots
#For examples, load packages and prepare data
library("phyloseq"); packageVersion("phyloseq")## [1] '1.32.0'library("ggplot2"); packageVersion("ggplot2")## [1] '3.3.2'theme_set(theme_bw())#Plot a 300-taxa dataset
data("GlobalPatterns")
gpt <- subset_taxa(GlobalPatterns, Kingdom=="Bacteria")
gpt <- prune_taxa(names(sort(taxa_sums(gpt),TRUE)[1:300]), gpt)
plot_heatmap(gpt, sample.label="SampleType")## Warning: Transformation introduced infinite values in discrete y-axis
#Subset a smaller dataset based on an Archaeal phylum
gpac <- subset_taxa(GlobalPatterns, Phylum=="Crenarchaeota")#Default plot_heatmap settings
plot_heatmap(gpac)## Warning: Transformation introduced infinite values in discrete y-axis
#Re-label by a sample variable and taxonomic family
(p <- plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family"))## Warning: Transformation introduced infinite values in discrete y-axis
#Re-label axis titles
p$scales$scales[[1]]$name <- "My X-Axis"
p$scales$scales[[2]]$name <- "My Y-Axis"
print(p)## Warning: Transformation introduced infinite values in discrete y-axis
#Changing the color scheme
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#CCFF66")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#FF3300")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#66CCFF")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#66CCFF", high="#000033", na.value="white")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#FFFFCC", high="#000033", na.value="white")## Warning: Transformation introduced infinite values in discrete y-axis
#Different ordination methods, distances
plot_heatmap(gpac, "NMDS", "jaccard")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "DCA", "none", "SampleType", "Family")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "RDA", "none", "SampleType", "Family")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "PCoA", "bray", "SampleType", "Family")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "PCoA", "unifrac", "SampleType", "Family")## Warning: Transformation introduced infinite values in discrete y-axis
plot_heatmap(gpac, "MDS", "unifrac", "SampleType", "Family", weighted=TRUE)## Warning: Transformation introduced infinite values in discrete y-axis
heatmap(otu_table(gpac))
###Networks #Load example data
data(enterotype)set.seed(711L)enterotype = subset_samples(enterotype, !is.na(Enterotype))#Plot_net function
plot_net(enterotype, maxdist = 0.4, point_label = "Sample_ID")
plot_net(enterotype, maxdist = 0.3, color = "SeqTech", shape="Enterotype")
#The plot_network function
ig <- make_network(enterotype, max.dist=0.3)plot_network(ig, enterotype)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)
ig <- make_network(enterotype, max.dist=0.2)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)
ig <- make_network(enterotype, dist.fun="bray", max.dist=0.3)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)